%0 Journal Article
	%A Victor Bodell and  Lukas Ekstrom and  Somayeh Aghanavesi
	%D 2021
	%J International Journal of Transport and Vehicle Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 170, 2021
	%T Comparing Machine Learning Estimation of Fuel Consumption of Heavy-Duty Vehicles
	%U https://publications.waset.org/pdf/10011850
	%V 170
	%X Fuel consumption (FC) is one of the key factors in
determining expenses of operating a heavy-duty vehicle. A customer
may therefore request an estimate of the FC of a desired vehicle.
The modular design of heavy-duty vehicles allows their construction
by specifying the building blocks, such as gear box, engine and
chassis type. If the combination of building blocks is unprecedented,
it is unfeasible to measure the FC, since this would first r equire the
construction of the vehicle. This paper proposes a machine learning
approach to predict FC. This study uses around 40,000 vehicles
specific and o perational e nvironmental c onditions i nformation, such
as road slopes and driver profiles. A ll v ehicles h ave d iesel engines
and a mileage of more than 20,000 km. The data is used to investigate
the accuracy of machine learning algorithms Linear regression (LR),
K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in
predicting fuel consumption for heavy-duty vehicles. Performance of
the algorithms is evaluated by reporting the prediction error on both
simulated data and operational measurements. The performance of the
algorithms is compared using nested cross-validation and statistical
hypothesis testing. The statistical evaluation procedure finds that
ANNs have the lowest prediction error compared to LR and KNN
in estimating fuel consumption on both simulated and operational
data. The models have a mean relative prediction error of 0.3% on
simulated data, and 4.2% on operational data.
	%P 97 - 101